1. Field of the Invention
The present invention relates to a broadcast receiver, and more particularly, to an apparatus for removing a noise of a video signal. Although the present invention is suitable for a wide scope of applications, it is particularly suitable for noise reduction of the video signal.
2. Discussion of the Related Art
Generally, there always exist various noises such as a thermal noise of a camera, a noise in a transmission process and the like in a TV video signal. And, theses noises are the major factors of image quality degradation.
Hence, a TV receiving unit or the like carries out such a processing as a noise removal and the like. In doing so, if a level of a noise appended to a video is unknown, the processing cannot provide a correct result.
For instance, if a strong noise removal is carried out on a video having a very small noise, a detailed video component is removed to provide an unclear video, i.e., a blurred video.
On the other hand, if a weak noise removal processing is carried out on a video having a big noise, it is unable to sufficiently remove the noise appended to the video.
Hence, a noise reduction is a technique that is essential to high video quality.
A noise of a video signal is generally expressed as Formula 1.g(x, y)=f(x, y)+n(x, y)  [Formula 1]
In Formula 1, if a signal g(x, y) is measured by adding a noise signal n(x, y) to an original video signal f(x, y), it is assumed that the noise signal has a normalized distribution having σn2 with an average 0 like N(0, σn2).
In this case, a noise reduction is carried out by filtering in a manner of estimating the original video signal f(x, y) containing no noise from the measured video signal g(x, y).
For this, such a method as spatial filtering, temporal filtering, spatio-temporal filtering and the like is used. Theses methods are effective in reducing noises by computing to output an average, intermediate value, weight total and the like between a target pixel to be filtered and neighbor pixels in the vicinity of the target pixel.
However, by theses methods, a fine component of a video is blurred. In particular, in case that a video motion varies a lot according to a time, blurring turns into afterimage in the temporal or spatio-temporal filtering. Hence, the corresponding image quality is degraded.